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Reference for ultralytics/models/yolo/pose/predict.py

Note

Full source code for this file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/predict.py. Help us fix any issues you see by submitting a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.yolo.pose.predict.PosePredictor

Bases: DetectionPredictor

A class extending the DetectionPredictor class for prediction based on a pose model.

Example
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.pose import PosePredictor

args = dict(model='yolov8n-pose.pt', source=ASSETS)
predictor = PosePredictor(overrides=args)
predictor.predict_cli()
Source code in ultralytics/models/yolo/pose/predict.py
class PosePredictor(DetectionPredictor):
    """
    A class extending the DetectionPredictor class for prediction based on a pose model.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.pose import PosePredictor

        args = dict(model='yolov8n-pose.pt', source=ASSETS)
        predictor = PosePredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = 'pose'
        if isinstance(self.args.device, str) and self.args.device.lower() == 'mps':
            LOGGER.warning("WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
                           'See https://github.com/ultralytics/ultralytics/issues/4031.')

    def postprocess(self, preds, img, orig_imgs):
        """Return detection results for a given input image or list of images."""
        preds = ops.non_max_suppression(preds,
                                        self.args.conf,
                                        self.args.iou,
                                        agnostic=self.args.agnostic_nms,
                                        max_det=self.args.max_det,
                                        classes=self.args.classes,
                                        nc=len(self.model.names))

        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

        results = []
        for i, pred in enumerate(preds):
            orig_img = orig_imgs[i]
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
            pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
            pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
            img_path = self.batch[0][i]
            results.append(
                Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts))
        return results

postprocess(preds, img, orig_imgs)

Return detection results for a given input image or list of images.

Source code in ultralytics/models/yolo/pose/predict.py
def postprocess(self, preds, img, orig_imgs):
    """Return detection results for a given input image or list of images."""
    preds = ops.non_max_suppression(preds,
                                    self.args.conf,
                                    self.args.iou,
                                    agnostic=self.args.agnostic_nms,
                                    max_det=self.args.max_det,
                                    classes=self.args.classes,
                                    nc=len(self.model.names))

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    results = []
    for i, pred in enumerate(preds):
        orig_img = orig_imgs[i]
        pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
        pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
        pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
        img_path = self.batch[0][i]
        results.append(
            Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts))
    return results




Created 2023-07-16, Updated 2023-08-20
Authors: glenn-jocher (6), Laughing-q (1)